On the use and misuse of climate change projections in international development

Abstract

Climate resilience is increasingly prioritized by international development agencies and national governments. However, current approaches to informing communities of future climate risk are problematic. The predominant focus on end-of-century projections neglects more pressing development concerns, which relate to the management of shorter-term risks and climate variability, and constitutes a substantial opportunity cost for the limited financial and human resources available to tackle development challenges. When a long-term view genuinely is relevant to decisionmaking, much of the information available is not fit for purpose. Climate model projections are able to capture many aspects of the climate system and so can be relied upon to guide mitigation plans and broad adaptation strategies, but the use of these models to guide local, practical adaptation actions is unwarranted. Climate models are unable to represent future conditions at the degree of spatial, temporal, and probabilistic precision with which projections are often provided, which gives a false impression of confidence to users of climate change information. In this article, we outline these issues, review their history, and provide a set of practical steps for both the development and climate scientist communities to consider. Solutions to mobilize the best available science include a focus on decision-relevant timescales, an increased role for model evaluation and expert judgment and the integration of climate variability into climate change services. This article is categorized under: Climate and Development > Knowledge and Action in Development K E Y W O R D S climate change adaptation, climate change projections, climate resilience, climate services, international development

Storyline approach to the construction of regional climate change information

“…This perspective also involves an acknowledgement that climate-relevant decisions, especially at the local scale, are not usually made on the basis of climate change alone but involve many other changing factors, most of which are highly uncertain. If climate impacts I are a product of hazard H , vulnerability V and exposure E , then, conceptually, the anthropogenic changes in I can be represented as δI=δfalse(HVEfalse)=HVδE+HEδV+VEδH. It may well be that the largest terms on the right-hand side of (6.2) are the first two, where it is the combination of climate and weather variability with changing vulnerability and exposure that is the main determinant of climate risk [70]. In this case, the decision framework is not so much that of dealing with climate change as it is that of bringing climate information into decisions that need to be made in any case.…”

Making Society Climate Resilient: International Progress under the Global Framework for Climate Services

“…However, in many countries, issues such as the governance of climate data, or poor availability of climate data of high quality and long enough record length, are key limiting factors for initiating the process. While multidecadal climate change projections are essential for informing mitigation policy, consideration should be given as to whether such projections target the appropriate time scale needed for adaptation decisions in developing countries (Nissan et al 2019).…”

Strengthening the global response to climate change and infectious disease threats

“…The health sector has been slow to divest from fossil fuels and should lead by example. 3 54 55 Climate change mitigation in healthcare systems must be adopted universally to achieve collectively endorsed mitigation targets, and help for poorer countries for greening their health sectors should be part of this commitment. A by-product of responses to the covid-19 pandemic has been reduced emissions of greenhouse gases and other harmful co-pollutants.…”

“… 63 Educating policy makers and other stakeholders regarding modeling processes and interpretation of findings is also essential. 55 64 Challenges related to interpreting modeling results have been apparent in the covid-19 response, with software engineers, decision makers, and the public calling for more transparent sharing of evidence used to inform vital decisions, and policy makers struggling to interpret seemingly disparate recommendations based on different model outputs. Looking forward, funders need to consider data science and software engineering as key components of any scientific tool kit and transdisciplinary epidemiological taskforce.…”